{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load the dataset\n",
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_dataset=load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(cancer_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(cancer_dataset['data'],columns=cancer_dataset['feature_names'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Standardization\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data=scaler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
       "         2.75062224,  1.93701461],\n",
       "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
       "        -0.24388967,  0.28118999],\n",
       "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
       "         1.152255  ,  0.20139121],\n",
       "       ...,\n",
       "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
       "        -1.10454895, -0.31840916],\n",
       "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
       "         1.91908301,  2.21963528],\n",
       "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
       "        -0.04813821, -0.75120669]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Applying PCA Algorithms\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pca=pca.fit_transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.19283683,  1.94858307],\n",
       "       [ 2.3878018 , -3.76817174],\n",
       "       [ 5.73389628, -1.0751738 ],\n",
       "       ...,\n",
       "       [ 1.25617928, -1.90229671],\n",
       "       [10.37479406,  1.67201011],\n",
       "       [-5.4752433 , -0.67063679]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.30499079,  5.7013746 ])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second Principal Component')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(data_pca[:,0],data_pca[:,1],c=cancer_dataset['target'],cmap='plasma')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second Principal Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
